Methods › Computer Vision › Image Models › gMLP

gMLP

7 papers tagged archive 2025-07-28

Introduced by Hanxiao Liu et al. in Pay Attention to MLPs

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

gMLP is an MLP-based alternative to Transformers without self-attention, which simply consists of channel projections and spatial projections with static parameterization. It is built out of basic MLP layers with gating. The model consists of a stack of L blocks with identical size and structure. Let X ∈ℝ^(n ×d) be the token representations with sequence length n and dimension d. Each block is defined as:

Z=σ(X U), Z̃=s(Z), Y=Z̃ V

where σ is an activation function such as GeLU. U and V define linear projections along the channel dimension - the same as those in the FFNs of Transformers (e.g., their shapes are 768 ×3072 and 3072 ×768 for BERT_(base)).

A key ingredient is s(·), a layer which captures spatial interactions. When s is an identity mapping, the above transformation degenerates to a regular FFN, where individual tokens are processed independently without any cross-token communication. One of the major focuses is therefore to design a good s capable of capturing complex spatial interactions across tokens. This leads to the use of a Spatial Gating Unit which involves a modified linear gating.

The overall block layout is inspired by inverted bottlenecks, which define s(·) as a spatial depthwise convolution. Note, unlike Transformers, gMLP does not require position embeddings because such information will be captured in s(·).

PaperSource

Papers archive 2025-07-28

7 shown of 7, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 31 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Image Classification3
Instance Segmentation2
Object Detection2
Question Answering2
Semantic Segmentation2
object-detection2
All1
Classification1
Common Sense Reasoning1
Decoder1
Deep Learning1
Graph Representation Learning1
In-Context Learning1
Language Modeling1
Language Modelling1
Mixture-of-Experts1
Multi Label Text Classification1
Multi-Label Classification1
Multi-Label Text Classification1
Natural Language Inference1

Usage over time archive 2025-07-28

Papers per year tagged with gMLP: 2021 to 2022, peak 4 4 0 2021: 3 papers 2021 2022: 4 papers 2022
Papers per year the archive tags with this method, by the paper's archive date (7 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Image Models

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